(SMAC) [1]} (Figure REF b) is a suite of cooperative multi-agent environments based on StarCraft II. Each task consists of a single scenario with two confronting teams, one of which is controlled by the game bot and the other by our algorithm. The goal of the environment is therefore to defeat enemy team. SMAC offers 23 different scenarios with varying difficulty and number of agents. SMAC focuses on learning micromanagement of a group of agents, e.g. using terrain to your advantage as in corridor map or kite opponents as in 3s vs 3z map. Learning this behaviours requires both good exploration properties as well as solving credit assignment issue as agents need to disentangle global reward for their actions. One important property of the environment is that not all actions are available during agents decision making, e.g. attacking opponents that are out of reach. This necessitates world model to predict if the action is available or not based on latent state in order to train agents in imaginary rollouts. We use additional Bernoulli classifier similar to Discount predictor and train it on action masks provided by the environment for each agent.
